Social and competition stress detection with wristband physiological signals

Sevil, Mert; Hajizadeh, Iman; Samadi, Sediqeh; Feng, Jun‐e; Lazaro, C. Burgos; Frantz, Nicole; Xia, Yu; Brandt, Rachel; Maloney, Zacharie; Çınar, Ali

doi:10.1109/bsn.2017.7936002

Cited by 22 publications

(23 citation statements)

References 13 publications

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“…The recognition of emotions can also be done using wearable sensors [ 207 ] such as the E4 wristband, which is a wearable research device that provides the means to acquire physiological data in real time. Many studies [ 208 , 209 , 210 ] have indeed shown that one can detect stress by using the physiological data that this device provides, in particular HR and EDA data.…”

Section: State 4: Emotionsmentioning

confidence: 99%

Survey and Synthesis of State of the Art in Driver Monitoring

Halin

Verly

Droogenbroeck

2021

Sensors

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Road vehicle accidents are mostly due to human errors, and many such accidents could be avoided by continuously monitoring the driver. Driver monitoring (DM) is a topic of growing interest in the automotive industry, and it will remain relevant for all vehicles that are not fully autonomous, and thus for decades for the average vehicle owner. The present paper focuses on the first step of DM, which consists of characterizing the state of the driver. Since DM will be increasingly linked to driving automation (DA), this paper presents a clear view of the role of DM at each of the six SAE levels of DA. This paper surveys the state of the art of DM, and then synthesizes it, providing a unique, structured, polychotomous view of the many characterization techniques of DM. Informed by the survey, the paper characterizes the driver state along the five main dimensions—called here “(sub)states”—of drowsiness, mental workload, distraction, emotions, and under the influence. The polychotomous view of DM is presented through a pair of interlocked tables that relate these states to their indicators (e.g., the eye-blink rate) and the sensors that can access each of these indicators (e.g., a camera). The tables factor in not only the effects linked directly to the driver, but also those linked to the (driven) vehicle and the (driving) environment. They show, at a glance, to concerned researchers, equipment providers, and vehicle manufacturers (1) most of the options they have to implement various forms of advanced DM systems, and (2) fruitful areas for further research and innovation.

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Section: State 4: Emotionsmentioning

confidence: 99%

Survey and Synthesis of State of the Art in Driver Monitoring

Halin

Verly

Droogenbroeck

2021

Sensors

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“…Furthermore, measurement of stress can be done by deploying smart devices such as wristbands (see e.g. Sevil et al., 2017 ) in order to track the stress' temporal dynamics. Subsequently, these measurements can be cross-validated with questionnaires on stress experiences as presented at the end of the experiment.…”

Section: Directions For Future Workmentioning

confidence: 99%

Can a monologue-style ECA more effectively motivate eHealth users in initial distress than textual guidance?

Scholten

Kelders

Gemert-Pijnen

2021

Heliyon

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Stress is a prevalent issue amongst patients with chronic conditions. As eHealth interventions are gaining importance, it becomes more relevant to invoke the possibilities from the eHealth technology itself to provide motivational acts during experiences of stress as to enhance adherence to the intervention. Embodied Conversational Agents (ECA's) also known as ‘robots on screen’ can potentially provide a remedy. Within our eHealth experiment we applied a between-subjects design and experimentally studied the difference in appraisal of motivation and guidance. We deployed a functionally modest, monologue-style ECA and compared them with textual guidance. This way, we filtered out the considerable positive impact of interactive features that go along with dialogue-style ECA's. The study was carried out amongst eHealth users of which half were deliberately put in a stressful pre-condition. The rationale was two-sided; first, we hypothesized that it would induce a need for motivational support. Second, it would provide a fair representation of eHealth users in real life. Furthermore, we investigated hypothesized positive effects from a gender match between participant and ECA. The results demonstrated preferential ECA effects compared to text but only in the no stress conditions. Although our set-up controlled for user distraction by putting the facilitating ECA in a pane separate from the eHealth environment, we suspect that the enduring visual presence of the ECA during task completion had still inhibited distressed users. Discussing this phenomenon, our stance is that the hypothesis that ECA support is always superior to textual guidance is open for re-evaluation. Text may sometimes serve users equally well because it lacks human-like aspects that in stressful circumstances can become confrontational. We discuss the potential of ECA's to motivate, but also elaborate on the caveats. Further implications for the ECA, intervention adherence, and eHealth study fields are discussed in relation to stress.

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“…The PTS machine learning model can also be retrained to classify AD using alternate smartwatch systems. If other sensors prove to be more accurate in the detection of the relevant physiological parameters [37], we believe the machine learning model would result in similar or more accurate detection of AD symptoms.…”

Section: A Using a Multimodal Approach To Detect Ad Symptomsmentioning

confidence: 99%

Automated Detection of Symptomatic Autonomic Dysreflexia Through Multimodal Sensing

Suresh

Duerstock

2020

IEEE J. Transl. Eng. Health Med.

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Objective: Autonomic Dysreflexia (AD) is a potentially life-threatening syndrome which occurs in individuals with higher level spinal cord injuries (SCI). AD is caused by triggers which can lead to rapid escalation of pathophysiological responses and if the trigger is not removed, AD can be fatal. There is currently no objective, non-invasive and accurate monitoring system available to automatically detect the onset of AD symptoms in real time in a non-clinical setting. Technology or Method: We developed a user-independent method of symptomatic AD detection in real time with a wearable physiological telemetry system (PTS) and a machine learning model using data from eleven participants with SCI. Results: The PTS could detect onset of AD symptoms with an average accuracy of 94.10% and a false negative rate of 4.89%. Conclusions: The PTS can detect the onset of the symptoms AD with high sensitivity and specificity to assist people with SCIs in preventing the occurrence of AD. It would enable persons with high level SCIs to be more independent and pursue vocational activities while granting continuous medical oversight. Clinical Impact: The PTS could serve as a supplementary tool to current solutions to detect the onset of AD and prepare individuals who are newly injured to be better prepared for AD episodes. Moreover, it could be translated into a system to encourage individuals to practice better healthcare management to prevent future occurrences.

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Social and competition stress detection with wristband physiological signals

Cited by 22 publications

References 13 publications

Survey and Synthesis of State of the Art in Driver Monitoring

Survey and Synthesis of State of the Art in Driver Monitoring

Can a monologue-style ECA more effectively motivate eHealth users in initial distress than textual guidance?

Automated Detection of Symptomatic Autonomic Dysreflexia Through Multimodal Sensing

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